Presented by Amy Orsborn, University of Washington
Design considerations for closed-loop decoder adaptation algorithms in invasive BCIs
Closed-loop decoder adaptation (CLDA) is a powerful technique in invasive brain-computer interfaces. CLDA is frequently used to train and optimize decoding algorithms “in situ,” reducing errors in performance that arise due to differences between open loop decoder training and closed loop BCI operation. CLDA has also proven useful as a strategy to maintain performance over time despite non-stationary measurements (e.g. signal drift). Many design choices must be made when creating a CLDA algorithm, which will influence its overall performance and utility. These choices include the form of error signal used to guide algorithm retraining, the learning rules used to update the decoder, the timescale of decoder updates, and the decoder parameters to update. User-decoder interactions must also be considered when designing CLDA algorithms. In this talk, I will briefly survey these details. I will present a case-study of CLDA algorithms optimized to robustly and rapidly initialize a BCI decoder independent of initialization, and review the algorithm design choices that led to the success of these algorithms. I will also briefly touch on the use of CLDA for promoting co-adaptation between the brain and decoder.